{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Physical GPUs, 2 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.6.0+cu101'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets/\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PyTorch 1.6.0+cu101 Backend"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.set_backend(\"pytorch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6813 - acc: 0.9214 - val_loss: 1.0506 - val_acc: 0.7820 - time: 0.3235\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 730us/step - test_loss: 1.0131 - test_acc: 0.8220 - time: 7.2259e-04\n",
      "Test loss 1.0131, Test accuracy 82.20%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import DenseGCN\n",
    "model = DenseGCN(graph, attr_transform=\"normalize_attr\", device='GPU', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
